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Re: st: mlogtest after mlogit


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: mlogtest after mlogit
Date   Tue, 25 Oct 2011 11:57:49 +0100

If you look at the code of -mlogtest- the warning message that worries
you is when a helper program -_pecats- produces output that is
inconsistent. After your -mlogit- call you can run -_pecats- followed
by -return list-. In a well-behaved case, you will get output like
this in which r(numcats) matches the number of elements in r(catvals).
Your output will probably not match up. I don't know why that might
be, but I doubt that the category frequencies are somehow the issue.
(For "STATA" read "Stata".)

. _pecats

. return list

scalars:
            r(numcats) =  9
             r(refval) =  3

macros:
            r(catnms8) : "1 2 4 5 6 7 8 9 3"
            r(catvals) : "1 2 4 5 6 7 8 9 3"
             r(catnms) : "1 2 4 5 6 7 8 9 3"
              r(refnm) : "3"


On Tue, Oct 25, 2011 at 11:10 AM, Chiara Mussida <cmussida@gmail.com> wrote:
> On 25 October 2011 01:10, Richard Williams
> <richardwilliams.ndu@gmail.com> wrote:
>> At 05:59 AM 10/24/2011, Muhammad Anees wrote:
>>>
>>> have you tried -mlogtest, all- to verify if these categories have no
>>> other issues. Otherwise the test on -combine- might have resulted
>>> becaure some of the categories had small or no observations so
>>> checking the condition of equal coefficients from -comibe- did not
>>> work.
>>
>> I agree - I get nervous about using multiple-outcome commands like mlogit
>> with lots and lots of independent variables. You may be spreading the data
>> too thin. But, before taking this too much further, I'd like the original
>> poster to confirm that the most current version of mlogtest is indeed being
>> used. Otherwise we might be talking about a problem that was fixed 6 months
>> ago. Also, it might be good to present a frequency of the dependent
>> variable. Long and Freese's commands are sometimes pickier about coding than
>> Stata is, e.g. they sometimes don't like non-integer coding. Also, you would
>> see if some of the categories have very small frequency counts. Finally, I
>> would run a simple model with only one or two independent variables followed
>> by mlogtest. If the simple model works and the more complicated one doesn't,
>> that might indicate problems with one or more of the added variables or with
>> the data being spread too thin to do the test.
>>
>>
> Dear All, I confirm that my current version of mlogtest is and was the
> one indicated by Nick, precisely:
>
> . which mlogtest, all
>
> C:\Program Files\Stata12\ado\updates\m\mlogtest.ado
> *! version 1.7.6 jsl 2009-10-18
>
> in terms of model estimates, I guess that the issue is related to the
> relative frequency of my dependent variables categories':
>
>  ta transition
>
>  transition |      Freq.     Percent        Cum.
> ------------+-----------------------------------
>          1 |        271        0.70        0.70
>          2 |        132        0.34        1.04
>          3 |      1,119        2.90        3.94
>          4 |        379        0.98        4.93
>          5 |        722        1.87        6.80
>          6 |     13,959       36.17       42.97
>          7 |        388        1.01       43.98
>          8 |        168        0.44       44.41
>          9 |     21,450       55.59      100.00
> ------------+-----------------------------------
>      Total |     38,588      100.00
>
> e.g., categories 2 and 8 might be too small. Now, I cannot collapse my
> dep variable in a reduced number of categories and I hope that
> notwithstanding the STATA alert message after typing the test command
> (below I copied all the results) do not bias my results. ps: for the
> test N=25441 since the model estimates are referred to a subsample
> (aged 15-64) of the overall population (38588).
>
> mlogtest, c
>
> Problem determining number of categories.
>
> **** Wald tests for combining alternatives (N=25441)
>
>  Ho: All coefficients except intercepts associated with a given pair
>     of alternatives are 0 (i.e., alternatives can be combined).
>
> Alternatives tested|     chi2   df   P>chi2
> -------------------+------------------------
>       1-       2  |   18.576    5    0.002
>       1-       3  |    5.990    5    0.307
>       1-       4  |   13.565    5    0.019
>       1-       5  |  148.448 5 0.000
>       1-       6  |  178.434    5    0.000
>       1-       7  |   33.226    5    0.000
>       1-       8  |   68.938    5    0.000
>       1-       9  |  311.133    5    0.000
>       2-       3  |   19.911    5    0.001
>       2-       4  |   23.931    5    0.000
>       2-       5  |   68.619    5    0.000
>       2-       6  |   68.197    5    0.000
>       2-       7  |   24.027    5    0.000
>       2-       8  |   55.161    5    0.000
>       2-       9  |  127.081    5    0.000
>       3-       4  |   36.426    5    0.000
>       3-       5  |  438.391    5    0.000
>       3-       6  |  703.923    5    0.000
>       3-       7  |   59.553    5    0.000
>       3-       8  |  103.537    5    0.000
>       3-       9  | 1130.422    5    0.000
>       4-       5  |  197.002    5    0.000
>       4-       6  |  103.387    5    0.000
>       4-       7  |   91.631    5    0.000
>       4-       8  |  132.381    5    0.000
>       4-       9  |  530.943    5    0.000
>       5-       6  |  592.783    5    0.000
>       5-       7  |  192.566    5    0.000
>       5-       8  |  142.185    5    0.000
>       5-       9  |  281.162    5    0.000
>       6-       7  |  520.969    5    0.000
>       6-       8  |  430.911    5    0.000
>       6-       9  | 5946.722    5    0.000
>       7-       8  |   24.867    5    0.000
>       7-       9  |  231.357    5    0.000
>       8-       9  |   82.933    5    0.000
> --------------------------------------------
>

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